Abstract
Low-cost air quality sensors have shown great promise as a complement to high-cost reference and equivalent methods. Though not currently as accurate, their low barrier of entry and smaller form factor allow them to be deployed in greater numbers, thus enabling air quality measurements to be made at a far higher spatial and temporal resolution than previously possible. However, their measurements require corrections as they suffer from both short-term biases (e.g., changes in environmental conditions such as temperature and humidity), and long-term measurement drift due to degradation. Many studies have focused on calibration and re-calibration of sensors, but fewer focus on correcting pre-calibrated sensor measurements. Correcting measurements is a likely scenario for people buying off-the-shelf devices, as they will not have access to the raw data that underpins the measurements, such as sensor voltages. Previous studies focused on a small range of correction techniques, without accounting for the variances that can occur between devices or locations. This work aimed to perform a comprehensive assessment of different correction techniques applied to air quality sensor systems. More than 470,000 unique measurement corrections were tested across two sites to determine best practices for correction campaigns going forward, resulting in a far more robust study than previous works. It highlights the large variances in results that occurred between sites, particularly for NO2, with results often more impacted by device type and location than the regression technique used. Simpler linear models were also found to perform just as well as, and sometimes better than, more complex non-parametric techniques. This study highlights that, though a strong focus is often put on comparing different regression methods, the choice of technique has less impact than the configuration of the device or the conditions of the co-location site. Therefore, future studies should focus less on small-scale comparisons of regression techniques and more on how to improve the transferability and applicability of results from a co-location campaign to another.